Implantable cardiac pacemakers have a great need to accurately process sensed signal information, so as to determine when a genuine cardiac signal has in fact been sensed, and then and to accurately identify, or classify the signal. Separating cardiac signals from polarization effects and other noise artifacts has always been a substantial problem, and a great deal of effort has been placed on improving input circuits for this purpose. Additionally, it is recognized that it is important to be able to classify a sensed signal, e.g., determine whether it is a QRS, P-wave, far field R-wave (FFRW), or what. Many prior art techniques have been developed for signal classification, but improvement is still needed. For example, one prior art technique is to establish a variable timing window, and classify the event in terms of the timing of the signal received during window. However, early beats; ectopic signals, etc. can fool such a technique, and noise can still mask the signal which is sensed within window. Other known techniques include morphology analysis, comparisons in the time and frequency domain, etc. While many of these techniques provide reasonably good results, they can involve considerable circuit complexity and frequently do not eliminate the probability of error due to detection of noise or other artifacts.
The advent of digital signal processing (DSP) has provided a tool which can be very useful in the environment of an implanted medical device, e.g., an implanted pacemaker. In DSP technology, the incoming sense signal is converted to a digital signal, e.g., an 8 bit signal at some sample rate. Successive digital signals can be processed with high reliability, in a manner which is essentially hardware-controlled by the DSP circuitry. More recently, DSP technology has advanced so as to provide the possibility of a low current chip which can be used in an implantable pacemaker to provide significant sensed signal processing capability.
The utilization of a DSP chip for an implantable pacemaker makes available an enhanced capability of processing sensed signals, so as to enable more accurate classification of the signal. Such DSP processing, together with a microprocessor and an appropriate signal classification algorithm, provides a powerful tool for accurately sensing and classifying intracardiac signals. In addition to this combined hardware and software capability, there is a need to provide an optimum decision algorithm for using the DSP-generated signal parameters so as to accurately and reliably classify sensed intracardiac signals.